电气技术  2024, Vol. 25 Issue (10): 8-14    DOI:
研究与开发 |
基于改进注意力机制的时间卷积网络-长短期记忆网络短期电力负荷预测
刘伟, 王洪志
东北石油大学电气信息工程学院,黑龙江 大庆 163000
Short term power load forecasting based on temporal convolutional network-long short term memory and improved attention mechanism
LIU Wei, WANG Hongzhi
College of Electrical Information Engineering, Northeast Petroleum University, Daqing, Heilongjiang 163000
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摘要 为充分挖掘蕴含在电力负荷数据中的有效时序信息,提高短期电力负荷预测准确度,本文提出一种基于改进注意力机制的时间卷积网络(TCN)-长短期记忆(LSTM)网络负荷预测模型。首先,将时序数据输入TCN模型中进行时序特征提取;然后,将所提取的时序特征与非时序数据组合,并输入LSTM模型中进行训练;最后,采用贝叶斯优化方法进行超参数寻优以获得TCN-LSTM模型的最优参数,引入通过多层感知器(MLP)改进的注意力机制以减少历史信息丢失并加强重要信息的影响,完成短期负荷预测。通过对比多种深度学习模型的预测效果表明,本文所提模型的短期电力负荷预测准确度更高。
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刘伟
王洪志
关键词 短期电力负荷预测改进注意力机制贝叶斯优化多层感知器(MLP)时间卷积网络(TCN)长短期记忆(LSTM)网络    
Abstract:To fully explore the effective temporal information contained in power load data and improve the accuracy of short term power load prediction, this article proposes a power load forecasting model based on an improved attention mechanism for the temporal convolutional network (TCN)-long short term memory (LSTM) network. Firstly, the temporal data is input into the TCN model to extract temporal features. Then, the extracted temporal features are combined with non temporal to be input into the LSTM model for training. Finally, Bayesian optimization method is used for hyperparameter optimization to get the best parameters in TCN-LSTM. An attention mechanism improved by multi-layer perceptron (MLP) is introduced to reduce the loss of historical information and strengthen the influence of important information, completing short term load forecasting. By comparing the predictive performance of various deep learning models, it is verified that the model proposed in this article has higher accuracy in short term power load forecasting.
Key wordsshort term power load forecasting    improved attention mechanism    Bayesian optimization    multi-layer perceptron (MLP)    time convolutional network (TCN)    long short term memory (LSTM) network   
收稿日期: 2024-04-02     
作者简介: 刘 伟(1971—),男,黑龙江哈尔滨人,博士,教授,主要研究方向为电力系统分析与智能控制、电机运行与控制、智能监测与诊断系统。
引用本文:   
刘伟, 王洪志. 基于改进注意力机制的时间卷积网络-长短期记忆网络短期电力负荷预测[J]. 电气技术, 2024, 25(10): 8-14. LIU Wei, WANG Hongzhi. Short term power load forecasting based on temporal convolutional network-long short term memory and improved attention mechanism. Electrical Engineering, 2024, 25(10): 8-14.
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